NOAA 29th Annual Climate Diagnostic & Prediction Workshop, October 2004, Madison, USA Operational Climate Monitoring from Space The Satellite Application Facility on Climate Monitoring (CM-SAF) presented by Jörg Schulz, Deutscher Wetterdienst contributions from: Peter Albert, Steven Dewitte, Annegret Gratzki, Rainer Hollmann, Karl-Göran Karlsson, Terhikki Manninen, Richard Müller, Rob Roebeling, and Werner Thomas SAF on Climate Monitoring: Visions

NOAA 29th Annual Climate Diagnostic & Prediction Workshop, October 2004, Madison, USA Outline 1. Network of Satellite Application Facilities and the CM-SAF 2. Meteosat Second Generation and MetOp 3. Products and Validation 4. Product Usage within Climate Sciences 5. Future Prospects

NOAA 29th Annual Climate Diagnostic & Prediction Workshop, October 2004, Madison, USA EUMETSAT's SAF network

NOAA 29th Annual Climate Diagnostic & Prediction Workshop, October 2004, Madison, USA The focus on climate aspects requires: a long term commitment to ascertain (EUMETSAT) and process (CM-SAF) data in an operational and reliable environment / mode Data processing and product generation with focus on climate aspects means: generate homogeneous data sets (space and time) carefully apply specific verification and validation procedures in order to achieve: quality controlled and quality assured products with appropriate condensation in space and time Why a dedicated CM-SAF?

NOAA 29th Annual Climate Diagnostic & Prediction Workshop, October 2004, Madison, USA CM-SAF has the mandate to generate thematic climate data records in an operational off-line environment. It requires calibrated and cross calibrated radiance data sets from different satellite operators. The products come in three major groups: Cloud parameters (Cloud Fractional Cover, Cloud Top Height, Cloud Type, Cloud Top Temperature, Cloud Phase, Cloud Optical Thickness, Cloud Water Path) Radiation budget parameters at the surface and TOA (Surface: Incoming Short Wave Radiation, Net Short Wave Radiation, Outgoing Long Wave Radiation, Downward Long Wave Radiation, Net Long Wave Radiation, Radiation Budget, Albedo (weekly); TOA: Incoming solar radiative flux, Reflected solar radiative flux, Emitted thermal radiative flux) Water vapour in the atmosphere (Total and layered precipitable water, temperature, and relative humidity) Product Groups

NOAA 29th Annual Climate Diagnostic & Prediction Workshop, October 2004, Madison, USA I 2004 I 2007 I 2005 I 2006 Initial Operations Phase V2 V3 MSG + EPS Area Extension Merging All products NOAA MSG HCP V1 NOAA Clouds Radiation CM- SAF: Schedule & Versioning

NOAA 29th Annual Climate Diagnostic & Prediction Workshop, October 2004, Madison, USA Meteosat Second Generation MetOp as part of the Initial Joint Polar System

NOAA 29th Annual Climate Diagnostic & Prediction Workshop, October 2004, Madison, USA - Operational since 29 January spectral channels - Full disk imagery every 15 minutes - Meteosat-9: launch in 2nd quarter of Two more satellites to follow <== for details see paper in BAMS, 2002 Courtesy of Jo Schmetz Meteosat-8 (first of Meteosat Second Generation)

NOAA 29th Annual Climate Diagnostic & Prediction Workshop, October 2004, Madison, USA Chl 4: 3.9 µm Chl 5: 6.2 µm Chl 6: 7.3 µm Chl 7: 8.7 µm Chl 8: 9.7 µm Chl 9: 10.8 µm Chl 10: 12.0 µm Chl 11: 13. µm Courtesy of Jo Schmetz Eight channels in thermal infrared spectrum

NOAA 29th Annual Climate Diagnostic & Prediction Workshop, October 2004, Madison, USA Calibration validation for thermal IR channels 0.2 K 0.4 K 0.3 K 1.5 K -0.3 K -0.7 K 0.9 K IR 3.9IR 6.2IR 7.3IR 9.7IR 10.8IR 12.0IR 13.4 Kelvin Comparison to HIRS measurements Courtesy of Jo Schmetz

NOAA 29th Annual Climate Diagnostic & Prediction Workshop, October 2004, Madison, USA MSG-1 GERB first image - 12 December 2002 Total channelShort wavelength channel

NOAA 29th Annual Climate Diagnostic & Prediction Workshop, October 2004, Madison, USA Instrument Payload of the Metop Satellites

NOAA 29th Annual Climate Diagnostic & Prediction Workshop, October 2004, Madison, USA Products and Validation

NOAA 29th Annual Climate Diagnostic & Prediction Workshop, October 2004, Madison, USA Examples - Cloud Fractional Cover feb04mar04 apr04may04

NOAA 29th Annual Climate Diagnostic & Prediction Workshop, October 2004, Madison, USA Examples - CFC partially cloudy pixels feb04mar04 apr04may04

NOAA 29th Annual Climate Diagnostic & Prediction Workshop, October 2004, Madison, USA Comparison and resulting problems European Cloud Climatology Courtesy of R. Meerkötter, DLR Main results: higher cloudiness over water surfaces satellite results systematically lower than corresponding results from synop. stations - even lower over land (Possible) reasons: (much) higher contrast over water  scenery effect (  ) ? observation rules ? threshold algorithms exhausted ? Way out: (more) physical retrieval ? further tuning ? new methods using temporal evolution ?

NOAA 29th Annual Climate Diagnostic & Prediction Workshop, October 2004, Madison, USA Examples - CTY - May 6th, :45 06:45 12:45 18:45

NOAA 29th Annual Climate Diagnostic & Prediction Workshop, October 2004, Madison, USA Cloud Optical Thickness & Cloud Liquid Water Path Cloud Properties for Cabauw, The Netherlands, 19 April 2004 Instantaneous average CLWP Meteosat-8 (10:00 utc) = 15 gm -2 Noaa-17 (10:08 utc) = 22 gm -2 Daily CLWP Meteosat-8 Average = 73 g.m -2 Std = 78 g.m -2 MSG NOAA

NOAA 29th Annual Climate Diagnostic & Prediction Workshop, October 2004, Madison, USA Reflected solar flux March 04Emitted thermal flux March 04 Examples - Reflected & Emitted TOA flux

NOAA 29th Annual Climate Diagnostic & Prediction Workshop, October 2004, Madison, USA Top of atmosphere emitted flux March 2004 Monthly mean diurnal cycle

NOAA 29th Annual Climate Diagnostic & Prediction Workshop, October 2004, Madison, USA Examples - Surface Albedo July04

NOAA 29th Annual Climate Diagnostic & Prediction Workshop, October 2004, Madison, USA Examples - Surface Downward Flux Feb 04 Mar 04 Apr 04May 04

NOAA 29th Annual Climate Diagnostic & Prediction Workshop, October 2004, Madison, USA HCP processing chain (V3) ATOVS IASI AIRS SEVIRI TPW 3 layer PW TPW 5 layer PW, RH, T 6 level T,q Create Daily average 3 km 2 sinusoidal projection Create Daily TPW, LPW, RH for each source in x km 2 sinusoidal projection Temporal sampling correction MERGER II superimpose merged vertical structure and TPW onto SEVIRI spatial sampling AMSU/B MHS SSM/I SSMIS TPW 5 layer PW, T GRAS TPW n layer PW n level T, q MERGER I Optimum interpolation of daily fields on 45 km grid Merged products High resolution products Q/A

NOAA 29th Annual Climate Diagnostic & Prediction Workshop, October 2004, Madison, USA ATOVS, June 8, 2004 TPW 850 hPa hPa Rel. 850 hPa hPa 850 hPa hPa Cloud Fraction (15 (km) 2 )

NOAA 29th Annual Climate Diagnostic & Prediction Workshop, October 2004, Madison, USA SEVIRI only products

NOAA 29th Annual Climate Diagnostic & Prediction Workshop, October 2004, Madison, USA Operational meteorological networks of the NMHSs Special networks as e.g. BSRN, EUREF, GUAN Research sites as e.g. Cabauw, Sodankylä, Lindenberg, Valencia Measurement campaigns (Sodankylä April 2004, BBC,BBC2, CNN I & II, Vapic, Lautlos, AMMA) Product validation data sources

NOAA 29th Annual Climate Diagnostic & Prediction Workshop, October 2004, Madison, USA Vertical albedo profiles 0 – 1000 m above various land use classes Surface albedo of three about 10 km * 10 km areas scanned with 1 km spacing with a flight altitude of 450 m and a flight speed of 50 km/h Calibration using albedo measurements at Sodankylä mast aapa mire boreal forest

NOAA 29th Annual Climate Diagnostic & Prediction Workshop, October 2004, Madison, USA Product validation - examples R.Roebeling, KNMI K.-G. Karlsson, SMHI T. Manninnen, FMI R. Hollmann, R. Müller, A. Gratzki, DWD

NOAA 29th Annual Climate Diagnostic & Prediction Workshop, October 2004, Madison, USA Data sets for water vapor validation Level 1 radiances: Use NWP monitoring systems, e.g. ECMWF data reception statistic; Level 2 instantaneous and level 3 products: ground based networks and reference sites via co-location, e.g., GPS, PMW, and radiosonde; field campaign data, e.g., BBC2 at Cabauw, VAPIC at SIRTA site in Palaiseau, France, 2004, AMMA (West Africa); other satellite data or algorithms, e.g, MODIS data or other SEVIRI algorithm; cross comparison to models. Q/A

NOAA 29th Annual Climate Diagnostic & Prediction Workshop, October 2004, Madison, USA Product Usage in Climate Sciences

NOAA 29th Annual Climate Diagnostic & Prediction Workshop, October 2004, Madison, USA Seasonal 2-D distribution of cloudiness for the entire SCANDIA area (Scandinavia) in the period SCANDIA RCA2 ERA-40 WINTER SPRING SUMMER AUTUMN SCANDIA: AVHRR-based cloud climatology (Karlsson, 2002, Int. J. Climatol., 23, ) RCA2: SMHI Rossby Centre regional climate simulation model version 2 (Jones et al, 2004, Ambio, 33, ) ERA-40: ECMWF Re-Analysis cloud dataset (Uppala, 2001, ECMWF Workshop Proc.)

NOAA 29th Annual Climate Diagnostic & Prediction Workshop, October 2004, Madison, USA New RCA3 results investigating the effect of optically very thin clouds WINTER SPRING SUMMER AUTUMN SCANDIA RCA3 original RCA3 filtered Purpose for filtering: SCANDIA cloud detection limit close to optical thickness of 1.0 Conclusion: New RCA3 version shows an unrealistic seasonal cycle with too large contribution from optically very thin clouds!

NOAA 29th Annual Climate Diagnostic & Prediction Workshop, October 2004, Madison, USA New RCA3 results investigating the effect of cloud overlap assumptions SCANDIA RCA3 Max-Random RCA3 Maximum WINTER SPRING SUMMER AUTUMN Purpose: Modelled cloudiness sensitive to way of combining clouds in different vertical grid layers. Conclusion: Maximum overlap gives less cloudiness (as expected). However, different overlap assumptions cannot explain basic problem with seasonal cycle!

NOAA 29th Annual Climate Diagnostic & Prediction Workshop, October 2004, Madison, USA Future Prospects

NOAA 29th Annual Climate Diagnostic & Prediction Workshop, October 2004, Madison, USA Some CM-SAF products will be global others not; Extension of the time series backwards; CM-SAF will have full re-processing capability; Existing products will be improved, mostly by providing error estimates; New parameters will be added, likely candidates are aerosols and precipitation; CM-SAF is looking for international partnership with international bodies, satellite operators, and science institutions to be part of a global integrated climate monitoring network. Key issues for the CM-SAF development

NOAA 29th Annual Climate Diagnostic & Prediction Workshop, October 2004, Madison, USA Updated CM-SAF internet pages October 04 Data search and ordering through Web User Interface ( ~ Jan 05)

NOAA 29th Annual Climate Diagnostic & Prediction Workshop, October 2004, Madison, USA Upcoming event: USER Workshop 2005